Senior Director, Systems Medicine Systems Medicine, Clinical Pharmacology & Quantitative Pharmacology, R&D BioPharmaceuticals, AstraZeneca, Cambridge, UK, United States
Objective: Cytokine Release Syndrome (CRS) is a common side effect for patients receiving bispecific T-cell engagers [1]. While specific cytokines (IL-6, IL-10, IFNg, TNFa) correlate with CRS severity, the underlying mechanisms behind patient-to-patient variation remain unclear. The aim of this work is trying to address this gap by developing a Quantitative Systems Pharmacology (QSP) approach to simulate cytokine release in oncology patients, aiming to help with the optimal dosing strategies and maximize the therapeutic benefit of these drugs.
Methods: The QSP model is based on ordinary differential equations (ODE) and built using MATLAB/SimBiology [2]. The work presented here is focused on the development of a model to simulate the response of non-Hodgkin lymphoma (NHL) patients treated with CD3xCD19 T-cell engagers (e.g., blinatumomab). Based on our own assumptions and hypotheses proposed in the literature (for example [3-4]), various levels of complexity in specific mechanisms (e.g., tumor composition, target expression, tumor burden, baseline cytokine levels and turnover) were evaluated in the QSP approach to describe the cytokine release and help to make theoretical comparisons of different dosing regimens, including dose escalation. The variability in tumor characteristics was explored in the model to account for the observed heterogeneity in cytokine response.
Results: The QSP approach allowed us to simulate cytokine levels in NHL patients, helping us to improve our understanding of CRS biology. We analyzed the impact of different hypotheses on patient cytokine variations. It was found that key factors like tumor composition, target expression, and tumor burden, which can vary significantly among patients, contributed to the observed heterogeneity in the response to treatment.
Conclusions: CRS is a complex adverse event. By unraveling the cytokine response, we could evaluate dosing regimens scenarios potentially improving treatment predictions. Our QSP model enhanced our mechanistic understanding of cytokine release, highlighting the critical role of tumor heterogeneity. By building a systems model and simulating various dosing scenarios, we could get a better interpretation of the influence of important factors promoting the perturbation in cytokine levels in plasma. Future work aims to integrate CRS severity data and patient characteristics for a more comprehensive model. Ultimately, this QSP approach is being developed as an additional tool to guide decision-making in the clinical space, helping with compound selection and the optimization of dosing strategies for specific indications.
Citations: [1] Cosenza M, Sacchi S, Pozzi S. Cytokine Release Syndrome Associated with T-Cell-Based Therapies for Hematological Malignancies: Pathophysiology, Clinical Presentation, and Treatment. Int J Mol Sci. 2021 Jul 17;22(14):7652 [2] https://www.mathworks.com/products/matlab.html [3] Weddell J. Mechanistically modeling peripheral cytokine dynamics following bispecific dosing in solid tumors. CPT Pharmacometrics Syst Pharmacol. 2023 Nov;12(11):1726-1737 [4] Hosseini I, Gadkar K, Stefanich E, Li CC, Sun LL, Chu YW, Ramanujan S. Mitigating the risk of cytokine release syndrome in a Phase I trial of CD20/CD3 bispecific antibody mosunetuzumab in NHL: impact of translational system modeling. NPJ Syst Biol Appl. 2020 Aug 28;6(1):28
